Moving cellular or subcellular object detection from temporal image sequence is the basic step for kinetic analysis of live cell time-lapse movies acquiring from video microscopes. It involves the accurate segmentation of moving cells from stationary background as well as the separation of cells when they touch each other.
Recognition of moving objects is one of the most important problems in computer vision. This problem has many applications in diverse disciplines including remote sensing, surveillance, medical diagnosis and treatment, civil infrastructure, and underwater sensing. Commonly used techniques for moving object detection in video images are dynamic models, temporal differencing and optical flow, background subtraction, etc. (J. Rittscher, J. Kato, S. Joga, and A. Blake “A probabilistic background model for tracking”. ECCV, pp. 336-350, 2000; D. Magee, ‘Tracking multiple vehicles using foreground, background and motion models’, in Proc. ECCV Workshop on Statistical Methods in Video Processing, (2002); Elgammal, R. Duraiswami, D. Harwood, and L. S. Davis, “Background and foreground modeling using nonparametric kernel density estimation for visual surveillance,” Proceedings of IEEE, vol. 90, pp. 1151-1163, 2002; Image change detection algorithms: a systematic survey Radke, R. J. Andra, S. Al-Kofahi, O. Roysam, B. Dept. of Electr., Rensselaer Polytech. Inst., Troy, N.Y., USA; C. Ridder, O. Munkelt, and H. Kirchner. Adaptive background estimation and foreground detection using kalman filtering. In Int. Conf. on Recent Advances in Mechatronics, pages 193-199, 1995.)
The prior art dynamic modeling method is only suitable for man-made objects or structurally predictable entities such as cars, airplanes or human (with head, arms, body, legs). They are not suitable for natural objects such as cells or other biological entities. The prior art temporal differencing and optical flow methods are very sensitive to noise due to its inherent high pass filtering characteristics and noise tends to be in the high frequency spectrum.
Background subtraction is a commonly used technique for moving object segmentation in static scenes. It attempts to detect moving regions by subtracting the current image pixel-by-pixel from a reference background image that is created by averaging images over time in an initialization period. The pixels where the difference is above a threshold are classified as foreground. The reference background is updated with new images over time to adapt to dynamic scene changes. However, the simple background subtraction or inter-frame differencing schemes are known to perform poorly. This is due to the inherent variations of the background image that cannot be easily compensated by a simple intensity background image.
Although background subtraction techniques could extract most of the relevant pixels of moving regions even when they stop, they are usually sensitive to dynamic changes such as sudden illumination changes. More advanced methods that make use of the temporal statistical characteristics of individual pixels have been developed in the prior art to overcome the shortcomings of basic background subtraction methods. These statistical methods are mainly inspired by the background subtraction methods in terms of keeping and dynamically updating statistics of the pixels that belong to the background image process. Moving objects are identified by comparing each pixel's statistics with that of the background model. This approach reduces false object detection. Yet, it suffers from missed detection of moving objects or regions of moving objects having low contrast with respect to the background intensity.